System-wide probabilistic alerting and activation

ABSTRACT

Systems, methods, and computer program products that enable system-wide probabilistic forecasting, alerting, optimizing and activating resources in the delivery of care to address both immediate (near real-time) conditions as well as probabilistic forecasted operational states of the system over an interval that is selectable from the current time to minutes, hours and coming days or weeks ahead are provided. There are multiple probabilistic future states that are implemented in these different time intervals and these may be implemented concurrently for an instant in time control, near term, and long term. Those forecasts along with their optimized control of hospital capacity may be independently calculated and optimized, such as for a dynamic workflow direction over the next hour and also a patient&#39;s stay over a period of days. In the present application, a probabilistic and conditional workflow reasoning system enabling complex team-based decisions that improve capacity, satisfaction, and safety is provided. A means to consume user(s) judgment, implement control on specific resource assignments and tasks in a clinical workflow is enabled, as is the dynamical and optimal control of the other care delivery assets being managed by the system so as to more probably achieve operating criteria such as throughput, waiting and schedule risk.

FIELD OF DISCLOSURE

The present disclosure relates to healthcare operations, and moreparticularly to systems, methods and computer program products toprovide system-wide probabilistic alerting and activation thatautomatically controls the resources and conditionally allocatescapacity across departments to achieve a minimal wait state through aninterval of time.

BACKGROUND

The statements in this section merely provide background informationrelated to the disclosure and may not constitute prior art.

A need in hospital operations management is creating an operationalcontrol center which reconciles the needs of individual departments forthe greater aims of more capacity at the institutional level. For thesetradeoffs to be rationally made, the interdependencies of the millionsof combinations of capacity, asset assignment, staff assignment andprotocol choice must be calculated and then reduced to a small number ofranked and feasible scenarios so that an automated control may directmost operations and then isolate the collaboration of cross functionalteams involved in managing the flow of patients and operations of thehospital with informed user intervention. These cross departmentaldecisions that benefit from analytical processing yet also need userjudgment may be improved from the current practice of “huddles” where noprobabilistic duration and little dynamic interdependencies can beconsidered. The byproduct of a lack of ability to forecast multiplefeasible futures results in the current practice of scheduling “slack”into core operations practice. This slack is used as a physical timebuffer to account for scheduling mismatching of patient services acrossdepartments when the departments do not have an ability to jointlyco-optimize sequencing. These departments include functions such asadmitting, bed management, scheduling, staffing, environmental systems,cleaning staff, transport dispatchers, diagnostic imaging, labs,physical therapy, meals and other related functions. Each of thesefunctional members has access to their function-specific transactionalIT systems and currently use them to perform their work. The presentdisclosure enables cross departmental operations automation and userintervention when needed.

In addition to their individual transactional systems, which may betheir current departmental applications, tone function of the disclosedsystem supports these teams in effective collaboration by displaying keydashboards from select sub-systems onto large screen displays that aremounted on the walls in a conference room during an operations huddle,typically conducted once or twice a day or in an operations center whichmay be a dedicated room or virtual presence. Each of these sourcesystems remain distinct and are often provided from multiple vendors.Users are left to determine the hundreds of interdependencies of apatient as the patient's care is planned and managed. Yet the actualco-optimization is impossible to reconcile with individual systems,especially when conditional branches or sequences are available in acare plan that traverses departments, as most do. Patients experiencethis lack of co-optimized control of assets and capacity as “hurry andwait”. Those delays accumulate and restrict hospital throughput.

It should be noted that in a hospital environment it can beconservatively estimated that if there were 250 rolling patients, 5available pathways, 15 pathway steps, this results in a potentialreorder of 360,360 possible combinations. Assuming it would take someone30 seconds to run one combination, it would take 20,568 person years tochoose the correct combination. Being infeasible, the tradition is tocreate rules of thumb or standard policies that build in slack or marginof operational error.

BRIEF SUMMARY

In view of the above, there is a need for a hospital control system thatestimates task durations, reconciles resource interdependencies andautomatically finds the optimal sequence and assignment of assets andresources for many patients across multiple departments through aforecast interval. The disclosed systems, methods, and computer programproducts perform system-wide probabilistic alerting and activation. Theabove-mentioned needs are addressed by the subject matter describedherein and will be understood in the following specification.

According to one aspect of the present disclosure, a system that allowssystem-wide probabilistic alerting and operations control activation isprovided.

According to another aspect of the present disclosure, a method thatallows system-wide probabilistic forecasting, alerting, optimization andactivation is provided.

This summary briefly describes aspects of the subject matter describedbelow in the Detailed Description, and is not intended to be used tolimit the scope of the subject matter described in the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and technical aspects of the system and method disclosedherein will become apparent in the following Detailed Description setforth below when taken in conjunction with the drawings in which likereference numerals indicate identical or functionally similar elements.

FIG. 1 shows a block diagram of an example healthcare-focusedinformation system.

FIG. 2 shows a block diagram of an example healthcare informationinfrastructure including one or more systems.

FIG. 3 is a block diagram illustrating an example cross-systemprobabilistic alerting and activation system, according to the presentdisclosure.

FIG. 4 shows a flow diagram illustrating an example method of operatingthe system of FIG. 3, according to the present disclosure.

FIG. 5 shows examples of throughput and schedule risk in an examplecross-system probabilistic alerting and activation system, according tothe present disclosure.

FIG. 6 shows examples of a delivery system with no variation, withvariation and the probability density function in an examplecross-system probabilistic alerting and activation system, according tothe present disclosure.

FIG. 7 shows examples of a method of creating predictive forecastassumptions used in the direction of people, assets and consumables inan example cross-system probabilistic alerting and activation system,according to the present disclosure.

FIG. 8 shows an analytical flow diagram of the hospital control systemin an example cross-system probabilistic alerting and activation system,according to the present disclosure.

FIG. 9 show an example path enumeration and probability estimationmethod.

FIG. 10 shows an example network diagram for feasible paths of anexample system.

FIG. 11 show temporal and logical relationships incorporating themethods described herein across departments and time, therebyillustrating how the benefits of manual and automatic resource levelingand dynamic workflow and scheduling is benefited.

FIG. 12 shows a course ranking method for a dynamic environment spanninga plurality of time periods using an example impact metric related totime duration for services delivery which is typically a key hospitaloperations process indicator.

FIG. 13 shows a block diagram of an example processor system that can beused to implement systems and methods described herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific examples that may be practiced. Theseexamples are described in sufficient detail to enable one skilled in theart to practice the subject matter, and it is to be understood thatother examples may be utilized and that logical, mechanical, electricaland other changes may be made without departing from the scope of thesubject matter of this disclosure. The following detailed descriptionis, therefore, provided to describe an exemplary implementation and notto be taken as limiting on the scope of the subject matter described inthis disclosure. Certain features from different aspects of thefollowing description may be combined to form yet new aspects of thesubject matter discussed below.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

I. Overview

Aspects disclosed and described herein enable system-wide probabilisticalerting and activation to address both immediate (near real-time)conditions as well as probabilistic forecasted operational states of thesystem over an interval that is selectable from the current time tominutes, hours and coming days or weeks ahead. There are multipleprobabilistic future states that are implemented in these different timeintervals and these may be implemented concurrently for an instant intime control, near term, and long term. Those forecasts along with theiroptimized control of hospital capacity may be independently calculatedand optimized, such as for a dynamic workflow direction over the nexthour and also a patient's stay over a period of days. In the presentapplication, a probabilistic and conditional workflow reasoning systemenabling complex team based decisions that improve capacity,satisfaction, and safety is considered.

Other aspects, such as those discussed in the following and others ascan be appreciated by one having ordinary skill in the art upon readingthe enclosed description, are also possible.

II. Example Operating Environment

Health information, also referred to as healthcare information and/orhealthcare data, relates to information generated and/or used by ahealthcare entity. Health information can be information associated withhealth of one or more patients, for example. Health information mayinclude protected health information (PHI), as outlined in the HealthInsurance Portability and Accountability Act (HIPAA), which isidentifiable as associated with a particular patient and is protectedfrom unauthorized disclosure. Health information can be organized asinternal information and external information. Internal informationincludes patient encounter information (e.g., patient-specific data,aggregate data, comparative data, etc.) and general healthcareoperations information, etc. External information includes comparativedata, expert and/or knowledge-based data, etc. Information can have botha clinical (e.g., diagnosis, treatment, prevention, etc.) andadministrative (e.g., scheduling, billing, management, etc.) purpose.

Institutions, such as healthcare institutions, having complex networksupport environments and sometimes chaotically driven process flowsutilize secure handling and safeguarding of the flow of sensitiveinformation (e.g., personal privacy). A need for secure handling andsafeguarding of information increases as a demand for flexibility,volume, and speed of exchange of such information grows. For example,healthcare institutions provide enhanced control and safeguarding of theexchange and storage of sensitive patient PHI and employee informationbetween diverse locations to improve hospital operational efficiency inan operational environment typically having a chaotic-driven demand bypatients for hospital services. In certain examples, patient identifyinginformation can be masked or even stripped from certain data dependingupon where the data is stored and who has access to that data. In someexamples, PHI that has been “de-identified” can be re-identified basedon a key and/or other encoder/decoder.

A healthcare information technology infrastructure can be adapted toservice multiple business interests while providing clinical informationand services. Such an infrastructure may include a centralizedcapability including, for example, a data repository, reporting,discreet data exchange/connectivity, “smart” algorithms,personalization/consumer decision support, etc. This centralizedcapability provides information and functionality to a plurality ofusers including medical devices, electronic records, access portals, payfor performance (P4P), chronic disease models, and clinical healthinformation exchange/regional health information organization(HIE/RHIO), and/or enterprise pharmaceutical studies, home health, forexample.

Interconnection of multiple data sources helps enable an engagement ofall relevant members of a patient's care team and helps improve anadministrative and management burden on the patient for managing his orher care. Particularly, interconnecting the patient's electronic medicalrecord and/or other medical data can help improve patient care andmanagement of patient information. Furthermore, patient care complianceis facilitated by providing tools that automatically adapt to thespecific and changing health conditions of the patient and providecomprehensive education and compliance tools to drive positive healthoutcomes.

In certain examples, healthcare information can be distributed amongmultiple applications using a variety of database and storagetechnologies and data formats. To provide a common interface and accessto data residing across these applications, a connectivity framework(CF) can be provided which leverages common data and service models (CDMand CSM) and service oriented technologies, such as an enterpriseservice bus (ESB) to provide access to the data.

In certain examples, a variety of user interface frameworks andtechnologies can be used to build applications for health informationsystems including, but not limited to, MICROSOFT® ASP.NET, AJAX®,MICROSOFT® Windows Presentation Foundation, GOOGLE® Web Toolkit,MICROSOFT® Silverlight, ADOBE®, and others. Applications can be composedfrom libraries of information widgets to display multi-content andmulti-media information, for example. In addition, the framework enablesusers to tailor layout of applications and interact with underlyingdata.

In certain examples, an advanced Service-Oriented Architecture (SOA)with a modern technology stack helps provide robust interoperability,reliability, and performance. Example SOA includes a three-foldinteroperability strategy including a central repository (e.g., acentral repository built from Health Level Seven (HL7) transactions),services for working in federated environments, and visual integrationwith third-party applications. Certain examples provide portable contentenabling plug ‘n play content exchange among healthcare organizations. Astandardized vocabulary using common standards (e.g., LOINC, SNOMED CT,RxNorm, FDB, ICD-9, ICD-10, etc.) is used for interoperability, forexample. Certain examples provide an intuitive user interface to helpminimize end-user training. Certain examples facilitate user-initiatedlaunching of third-party applications directly from a desktop interfaceto help provide a seamless workflow by sharing user, patient, and/orother contexts. Certain examples provide real-time (or at leastsubstantially real time assuming some system delay) patient data fromone or more information technology (IT) systems and facilitatecomparison(s) against evidence-based best practices. Certain examplesprovide one or more dashboards for specific sets of patients.Dashboard(s) can be based on condition, role, and/or other criteria toindicate variation(s) from a desired practice, for example.

A. Example Healthcare Information System

An information system can be defined as an arrangement ofinformation/data, processes, and information technology that interact tocollect, process, store, and provide informational output to supportdelivery of healthcare to one or more patients. Information technologyincludes computer technology (e.g., hardware and software) along withdata and telecommunications technology (e.g., data, image, and/or voicenetwork, etc.).

Turning now to the figures, FIG. 1 shows a block diagram of an examplehealthcare-focused information system 100. Example system 100 can beconfigured to implement a variety of systems and processes includingimage storage (e.g., picture archiving and communication system (PACS),etc.), image processing and/or analysis, radiology reporting and/orreview (e.g., radiology information system (RIS), etc.), computerizedprovider order entry (CPOE) system, clinical decision support, patientmonitoring, population health management (e.g., population healthmanagement system (PHMS), health information exchange (HIE), etc.),healthcare data analytics, cloud-based image sharing, electronic medicalrecord (e.g., electronic medical record system (EMR), electronic healthrecord system (EHR), electronic patient record (EPR), personal healthrecord system (PHR), etc.), and/or other health information system(e.g., clinical information system (CIS), hospital information system(HIS), patient data management system (PDMS), laboratory informationsystem (LIS), cardiovascular information system (CVIS), etc.

As illustrated in FIG. 1, the example information system 100 includes aninput 110, an output 120, a processor 130, a memory 140, and acommunication interface 150. The components of example system 100 can beintegrated in one device or distributed over two or more devices.

Example input 110 may include a keyboard, a touch-screen, a mouse, atrackball, a track pad, optical barcode recognition, voice command, etc.or combination thereof used to communicate an instruction or data tosystem 100. Example input 110 may include an interface between systems,between user(s) and system 100, etc.

Example output 120 can provide a display generated by processor 130 forvisual illustration on a monitor or the like. The display can be in theform of a network interface or graphic user interface (GUI) to exchangedata, instructions, or illustrations on a computing device viacommunication interface 150, for example. Example output 120 may includea monitor (e.g., liquid crystal display (LCD), plasma display, cathoderay tube (CRT), etc.), light emitting diodes (LEDs), a touch-screen, aprinter, a speaker, or other conventional display device or combinationthereof.

Example processor 130 includes hardware and/or software configuring thehardware to execute one or more tasks and/or implement a particularsystem configuration. Example processor 130 processes data received atinput 110 and generates a result that can be provided to one or more ofoutput 120, memory 140, and communication interface 150. For example,example processor 130 can take user annotation provided via input 110with respect to an image displayed via output 120 and can generate areport associated with the image based on the annotation. As anotherexample, processor 130 can process updated patient information obtainedvia input 110 to provide an updated patient record to an EMR viacommunication interface 150.

Example memory 140 may include a relational database, an object-orienteddatabase, a data dictionary, a clinical data repository, a datawarehouse, a data mart, a vendor neutral archive, an enterprise archive,etc. Example memory 140 stores images, patient data, best practices,clinical knowledge, analytics, reports, etc. Example memory 140 canstore data and/or instructions for access by the processor 130. Incertain examples, memory 140 can be accessible by an external system viathe communication interface 150.

In certain examples, memory 140 stores and controls access to encryptedinformation, such as patient records, encrypted update-transactions forpatient medical records, including usage history, etc. In an example,medical records can be stored without using logic structures specific tomedical records. In such a manner, memory 140 is not searchable. Forexample, a patient's data can be encrypted with a unique patient-ownedkey at the source of the data. The data is then uploaded to memory 140.Memory 140 does not process or store unencrypted data thus minimizingprivacy concerns. The patient's data can be downloaded and decryptedlocally with the encryption key.

For example, memory 140 can be structured according to provider,patient, patient/provider association, and document. Providerinformation may include, for example, an identifier, a name, andaddress, a public key, and one or more security categories. Patientinformation may include, for example, an identifier, a password hash,and an encrypted email address. Patient/provider association informationmay include a provider identifier, a patient identifier, an encryptedkey, and one or more override security categories. Document informationmay include an identifier, a patient identifier, a clinic identifier, asecurity category, and encrypted data, for example.

Example communication interface 150 facilitates transmission ofelectronic data within and/or among one or more systems. Communicationvia communication interface 150 can be implemented using one or moreprotocols. In some examples, communication via communication interface150 occurs according to one or more standards (e.g., Digital Imaging andCommunications in Medicine (DICOM), Health Level Seven (HL7), ANSI X12N,etc.). Example communication interface 150 can be a wired interface(e.g., a data bus, a Universal Serial Bus (USB) connection, etc.) and/ora wireless interface (e.g., radio frequency, infrared, near fieldcommunication (NFC), etc.). For example, communication interface 150 maycommunicate via wired local area network (LAN), wireless LAN, wide areanetwork (WAN), etc. using any past, present, or future communicationprotocol (e.g., BLUETOOTH™, USB 2.0, USB 3.0, etc.).

In certain examples, a Web-based portal may be used to facilitate accessto information, patient care and/or practice management, etc.Information and/or functionality available via the Web-based portal mayinclude one or more of order entry, laboratory test results reviewsystem, patient information, clinical decision support, medicationmanagement, scheduling, electronic mail and/or messaging, medicalresources, etc. In certain examples, a browser-based interface can serveas a zero footprint, zero download, and/or other universal viewer for aclient device.

In certain examples, the Web-based portal serves as a central interfaceto access information and applications, for example. Data may be viewedthrough the Web-based portal or viewer, for example. Additionally, datamay be manipulated and propagated using the Web-based portal, forexample. Data may be generated, modified, stored and/or used and thencommunicated to another application or system to be modified, storedand/or used, for example, via the Web-based portal, for example.

The Web-based portal may be accessible locally (e.g., in an office)and/or remotely (e.g., via the Internet and/or other private network orconnection), for example. The Web-based portal may be configured to helpor guide a user in accessing data and/or functions to facilitate patientcare and practice management, for example. In certain examples, theWeb-based portal may be configured according to certain rules,preferences and/or functions, for example. For example, a user maycustomize the Web portal according to particular desires, preferencesand/or requirements.

B. Example Healthcare Infrastructure

FIG. 2 shows a block diagram of an example healthcare informationinfrastructure 200 including one or more subsystems such as the examplehealthcare-related information system 100 illustrated in FIG. 1. Examplehealthcare system 200 includes a HIS 204, a RIS 206, a PACS 208, aninterface unit 210, a data center 212, and a workstation 214. In theillustrated example, HIS 204, RIS 206, and PACS 208 are housed in ahealthcare facility and locally archived. However, in otherimplementations, HIS 204, RIS 206, and/or PACS 208 may be housed withinone or more other suitable locations. In certain implementations, one ormore of PACS 208, RIS 206, HIS 204, etc., may be implemented remotelyvia a thin client and/or downloadable software solution. Furthermore,one or more components of the healthcare system 200 can be combinedand/or implemented together. For example, RIS 206 and/or PACS 208 can beintegrated with HIS 204; PACS 208 can be integrated with RIS 206; and/orthe three example information systems 204, 206, and/or 208 can beintegrated together. In other example implementations, healthcare system200 includes a subset of the illustrated information systems 204, 206,and/or 208. For example, healthcare system 200 may include only one ortwo of HIS 204, RIS 206, and/or PACS 208. Information (e.g., scheduling,test results, exam image data, observations, diagnosis, etc.) can beentered into HIS 204, RIS 206, and/or PACS 208 by healthcarepractitioners (e.g., radiologists, physicians, and/or technicians)and/or administrators before and/or after patient examination.

The HIS 204 stores medical information such as clinical reports, patientinformation, and/or administrative information received from, forexample, personnel at a hospital, clinic, and/or a physician's office(e.g., an EMR, EHR, PHR, etc.). RIS 206 stores information such as, forexample, radiology reports, radiology exam image data, messages,warnings, alerts, patient scheduling information, patient demographicdata, patient tracking information, and/or physician and patient statusmonitors. Additionally, RIS 206 enables exam order entry (e.g., orderingan x-ray of a patient) and image and film tracking (e.g., trackingidentities of one or more people that have checked out a film). In someexamples, information in RIS 206 is formatted according to the HL-7(Health Level Seven) clinical communication protocol. In certainexamples, a medical exam distributor is located in RIS 206 to facilitatedistribution of radiology exams to a radiologist workload for review andmanagement of the exam distribution by, for example, an administrator.

PACS 208 stores medical images (e.g., x-rays, scans, three-dimensionalrenderings, etc.) as, for example, digital images in a database orregistry. In some examples, the medical images are stored in PACS 208using the Digital Imaging and Communications in Medicine (DICOM) format.Images are stored in PACS 208 by healthcare practitioners (e.g., imagingtechnicians, physicians, radiologists) after a medical imaging of apatient and/or are automatically transmitted from medical imagingdevices to PACS 208 for storage. In some examples, PACS 208 can alsoinclude a display device and/or viewing workstation to enable ahealthcare practitioner or provider to communicate with PACS 208.

The interface unit 210 includes a hospital information system interfaceconnection 216, a radiology information system interface connection 218,a PACS interface connection 220, and a data center interface connection222. Interface unit 210 facilities communication among HIS 204, RIS 206,PACS 208, and/or data center 212. Interface connections 216, 218, 220,and 222 can be implemented by, for example, a Wide Area Network (WAN)such as a private network or the Internet. Accordingly, interface unit210 includes one or more communication components such as, for example,an Ethernet device, an asynchronous transfer mode (ATM) device, an802.11 device, a DSL modem, a cable modem, a cellular modem, etc. Inturn, the data center 212 communicates with workstation 214, via anetwork 224, implemented at a plurality of locations (e.g., a hospital,clinic, doctor's office, other medical office, or terminal, etc.).Network 224 is implemented by, for example, the Internet, an intranet, aprivate network, a wired or wireless Local Area Network, and/or a wiredor wireless Wide Area Network. In some examples, interface unit 210 alsoincludes a broker (e.g., a Mitra Imaging's PACS Broker) to allow medicalinformation and medical images to be transmitted together and storedtogether.

Interface unit 210 receives images, medical reports, administrativeinformation, exam workload distribution information, and/or otherclinical information from the information systems 204, 206, 208 via theinterface connections 216, 218, 220. If necessary (e.g., when differentformats of the received information are incompatible), interface unit210 translates or reformats (e.g., into Structured Query Language(“SQL”) or standard text) the medical information, such as medicalreports, to be properly stored at data center 212. The reformattedmedical information can be transmitted using a transmission protocol toenable different medical information to share common identificationelements, such as a patient name or social security number. Next,interface unit 210 transmits the medical information to data center 212via data center interface connection 222. Finally, medical informationis stored in data center 212 in, for example, the DICOM format, whichenables medical images and corresponding medical information to betransmitted and stored together.

The medical information is later viewable and easily retrievable atworkstation 214 (e.g., by their common identification element, such as apatient name or record number). Workstation 214 can be any equipment(e.g., a personal computer) capable of executing software that permitselectronic data (e.g., medical reports) and/or electronic medical images(e.g., x-rays, ultrasounds, MRI scans, etc.) to be acquired, stored, ortransmitted for viewing and operation. Workstation 214 receives commandsand/or other input from a user via, for example, a keyboard, mouse,track ball, microphone, etc. Workstation 214 is capable of implementinga user interface 226 to enable a healthcare practitioner and/oradministrator to interact with healthcare system 200. For example, inresponse to a request from a physician, user interface 226 presents apatient medical history. In other examples, a radiologist is able toretrieve and manage a workload of exams distributed for review to theradiologist via user interface 226. In further examples, anadministrator reviews radiologist workloads, exam allocation, and/oroperational statistics associated with the distribution of exams viauser interface 226. In some examples, the administrator adjusts one ormore settings or outcomes via user interface 226.

Example data center 212 of FIG. 2 is an archive to store informationsuch as images, data, medical reports, and/or, more generally, patientmedical records. In addition, data center 212 can also serve as acentral conduit to information located at other sources such as, forexample, local archives, hospital information systems/radiologyinformation systems (e.g., HIS 204 and/or RIS 206), or medicalimaging/storage systems (e.g., PACS 208 and/or connected imagingmodalities). That is, the data center 212 can store links or indicators(e.g., identification numbers, patient names, or record numbers) toinformation. In the illustrated example, data center 212 is managed byan application server provider (ASP) and is located in a centralizedlocation that can be accessed by a plurality of systems and facilities(e.g., hospitals, clinics, doctor's offices, other medical offices,and/or terminals). In some examples, data center 212 can be spatiallydistant from HIS 204, RIS 206, and/or PACS 208.

Example data center 212 of FIG. 2 includes a server 228, a database 230,and a record organizer 232. Server 228 receives, processes, and conveysinformation to and from the components of healthcare system 200.Database 230 stores the medical information described herein andprovides access thereto. Example record organizer 232 of FIG. 2 managespatient medical histories, for example. Record organizer 232 can alsoassist in procedure scheduling, for example.

Certain examples can be implemented as cloud-based clinical informationsystems and associated methods of use. An example cloud-based clinicalinformation system enables healthcare entities (e.g., patients,clinicians, sites, groups, communities, and/or other entities) to shareinformation via web-based applications, cloud storage and cloudservices. For example, the cloud-based clinical information system mayenable a first clinician to securely upload information into thecloud-based clinical information system to allow a second clinician toview and/or download the information via a web application. Thus, forexample, the first clinician may upload an x-ray image into thecloud-based clinical information system, and the second clinician mayview the x-ray image via a web browser and/or download the x-ray imageonto a local information system employed by the second clinician.

In certain examples, users (e.g., a patient and/or care provider) canaccess functionality provided by system 200 via a software-as-a-service(SaaS) implementation over a cloud or other computer network, forexample. In certain examples, all or part of system 200 can also beprovided via platform as a service (PaaS), infrastructure as a service(IaaS), etc. For example, system 200 can be implemented as acloud-delivered Mobile Computing Integration Platform as a Service. Aset of consumer-facing Web-based, mobile, and/or other applicationsenable users to interact with the PaaS, for example.

C. Example Methods of Use

Clinical workflows are typically defined to include one or more steps oractions to be taken in response to one or more events and/or accordingto a schedule. Events may include receiving a healthcare messageassociated with one or more aspects of a clinical record, opening arecord(s) for new patient(s), receiving a transferred patient, reviewingand reporting on an image, and/or any other instance and/or situationthat requires or dictates responsive action or processing. The actionsor steps of a clinical workflow may include placing an order for one ormore clinical tests, scheduling a procedure, requesting certaininformation to supplement a received healthcare record, retrievingadditional information associated with a patient, providing instructionsto a patient and/or a healthcare practitioner associated with thetreatment of the patient, radiology image reading, and/or any otheraction useful in processing healthcare information. The defined clinicalworkflows may include manual actions or steps to be taken by, forexample, an administrator or practitioner, electronic actions or stepsto be taken by a system or device, and/or a combination of manual andelectronic action(s) or step(s). While one entity of a healthcareenterprise may define a clinical workflow for a certain event in a firstmanner, a second entity of the healthcare enterprise may define aclinical workflow of that event in a second, different manner. In otherwords, different healthcare entities may treat or respond to the sameevent or circumstance in different fashions. Differences in workflowapproaches may arise from varying preferences, capabilities,requirements or obligations, standards, protocols, etc. among thedifferent healthcare entities.

In certain examples, a medical exam conducted on a patient can involvereview by a healthcare practitioner, such as a radiologist, to obtain,for example, diagnostic information from the exam. In a hospitalsetting, medical exams can be ordered for a plurality of patients, allof which require review by an examining practitioner. Each exam hasassociated attributes, such as a modality, a part of the human bodyunder exam, and/or an exam priority level related to a patientcriticality level. Hospital administrators, in managing distribution ofexams for review by practitioners, can consider the exam attributes aswell as staff availability, staff credentials, and/or institutionalfactors such as service level agreements and/or overhead costs.

Additional workflows can be facilitated such as bill processing, revenuecycle mgmt., population health management, patient identity, consentmanagement, etc.

III. Example System

FIG. 3 depicts an example control system 300 for probabilisticcross-system forecasting, alerting, optimization and either automatic oruser activation, according to one aspect of the present disclosure.System 300 includes a command center engine 302, which facilitates thecollaboration of cross functional teams using data from department-widesystems to manage the hospital operations. Certain aspects includefunctions such as admitting, bed management, scheduling, staffing,environmental systems, transport dispatchers and other relatedfunctions, for example.

In certain aspects, command center engine 302 has the ability to parsedata from multiple departmental transactional systems 304. A multitudeof hospital transactional systems are interfaced via various methods.For example, a real-time HL7 message broker for patient-relatedinformation and via an enterprise service bus for system datainformation. Data received from these systems is parsed, transformed andintegrated into the form of derived entity structures 306 in datainjection servers. In certain aspects, the derived entities and the rawdata is persisted in database servers. These derived entities 306 arequeried by multiple-purpose analytics engine 308 and also provide inputto a simulation-based prediction system 310. Simulation-based predictionsystem 310 provides probabilistic forecasts to multiple-purposeanalytics engine 308. The near real-time and forecasted information isused by multiple-purpose analytics engine 308 to generate informationabout the operational state of the hospital operations. Multiple-purposeengine 308 provides automated mitigation actions 314 to other hospitalsystems and personnel and also provides situational awareness androle-specific probabilistic warning and alert states 316. In certainaspects, information is provided to users in command center engine 302and elsewhere in the hospital system in the form of analytic tiles.Probabilistic alert information is used to take distinctive actions,based on rules, to mitigate defects and constraints automaticallydetected by the system.

As an example, command center engine 302 can use cross-system data,forecasts and probabilistic alerts to automatically and optimallycontrol patient discharge and environmental services activity to betterserve patients and improve hospital operational performance, such asreducing waiting delays for procedures and accelerating discharges. Datais extracted from multiple disparate systems 304, which for this examplemay include perioperative/procedural, DI, labs, therapy, transfercenter, emergency department (ED), bed management and admitting. Afterbeing parsed, transformed, and integrated into derived entities 306,calculations are performed by multiple-purpose analytics engine 308 andsimulation-based hospital operations prediction system 310 to identifystates that indicate how patient flow is currently occurring or willoccur in the forecasted future. The calculated defect states includewhen threshold time intervals are exceeded for conditions such as andnot limited to: 1) patients holding in the OR when another case isscheduled to follow; 2) patients who are boarding in ED awaiting aninpatient bed that matches their needs; 3) patients who are queued inthe transfer center to be transferred to the hospital from another carefacility to provide a higher level of care; 4) patients who are waitingin admissions because their physician said they needed to be admitted;5) patients who are dwelling PACU after having been medically cleared toproceed to the next level of care; 6) patients waiting for therapy,imaging and discharge. These defect states are calculated in real-timebut are also projected over a forecast interval such as, for example,the next 48 hours using inferences, probabilistic calculations and theresults of the simulation-based hospital operations prediction system310. In this example, defect states that are determined to be urgent areset with a configurable threshold and trigger the following events, asexamples, in the current time and/or the simulated forward future: 1)determine the hospital beds that are unoccupied and thus need to becleaned; 2) match the unit the bed resides with the clinical specialtyof the patient in need; 3) determine if the bed is the correct level ofcare for the patient (i.e., ICU vs. step down vs. acute, for example);4) put this bed on a list to be cleaned immediately and alert the bedmanagement team and the dispatcher for environmental services team ofthe urgent defect and suggested mitigation. Simultaneously, for eachpatient in the system with a status of Pending Discharge and for eachpatient identified by command center engine 302 as a potential discharge(for example, by examining derived entities 306 to determine if nursingor case management records suggest a patient meets clinical criteria forbeing a discharge candidate using probabilistic methods) determine if abed in which the discharge candidate patient resides matches theclinical discipline (specialty) of the patient in need and if the bedthe discharge candidate patient resides is the correct level of care forthe patient in need. When these conditions are identified, the dischargecandidate patient is accelerated for discharge activities and alerts areprovided to the bed management team, case management, and clinical teamon the unit of the urgent defect and suggested mitigation. The defectsstates and suggested mitigation are communicated via several methodsincluding on a “priority discharge tile” in display system 318 incommand center engine 302.

A detail of the hospital operations control system is illustrated inFIG. 4, the example system 400 details the method of dynamicallyoptimizing the hospital for the patients who are present and scheduledfor their care delivery and for optimal recovery of the resource andpatient schedule when an intervention is made, such as a manualassignment decision. The present system receives a preferred throughputand schedule risk, as input, from hospital administrators and controlsthe system to achieve those objectives to the fullest feasible extent.Controllable resources and controllable paths of sequences are definedas resources and paths of sequences that are able to be directlycontrolled. This is differentiated from those tasks, paths and resourceswhich cannot be controlled (non-controllable).

For example, one patient 402 presents to the hospital by way of ascheduled arrival or an Emergency Department entry. The patient istriaged and provided a care plan which is an ordered sequence of tasks412, 414, 416, 418, 428, 430, 432, 446, 448 that use and consumeresources provided by the healthcare delivery system. The arrangement ofthese tasks may be changed at a conditional branch 420 that iscontrolled by the optimization logic, which is configured to be goalseeking of designated criteria, in order to achieve the satisfaction ofthe care plan at a desired criteria objective such as minimal time,illustrated as the time difference between 456 and 407, or highestprobability of completing the plan by a point certain in future time 410or a combination of cycle time, throughput or time variance.

Other patients 404 exist in the healthcare enterprise being controlled.These patients may already be admitted and actively managed and/or theymay be future arrivals such as those who are scheduled for a clinicalprocedure, and/or they may be a randomized forecasted arrival sequenceof hypothetical patients with attributes that may be adjusted by thesystem to mimic a typical historical patient mix or a specificconcentration. The delivery of care by virtue of controlling theresources required 406 in the care plans of patients, is provided to theentire cohort of patients 402, 404 or any subset of the combinedpopulation at one or multiple time intervals in any combination ofduration between times 407, 408, 409, 411, 110.

The resources 406 required for care are controlled by the system 400.Examples of resources that the tasks of a care protocol requires includecare providers such as doctors, nurses, technicians; physical spacessuch as patient rooms, beds, operating rooms, diagnostic imaging suitesand lab space; and devices such as clinical apparatus or consumables.The locations of activities may be in one or more departments of ahospital, located in a single connected building, a campus or multiplefacilities such as affiliated clinics and a patient's monitored homelocation. These resources 406 may have assignment limitations such asmutual exclusivity of use—for example, one patient in an operating room,or limits on the resource to multi-task, for example, a certain numberof patients a nurse can care for. These constraints limit the assignmentof resources to patients and thus act to limit the feasibility ofexecuting a task that requires one or more of the resources. Thedisclosed system uses these limited resources as a control point in thatby assigning resources to tasks, care is delivered. By not assigning aresource, patients are not able to proceed through a certain task(s).One or more patients are affected by limited resources. The disclosedsystem is managing the one or more patients concurrently with respect tothe resource assignments and thus all combinations, which wouldoverwhelm human decision making, are computed of patients, care plantasks, resource assignments concurrently. Local and global optimalcontrol, given the patients, their care plans and resources is achieved.

In one aspect, a number of objectives are being controlled for. Forexample, one or more patient throughput for an interval of time and theprobability 606 (FIG. 6) that one or more patients will traverse thecare plan by a certain time point, say final time (t_(f)) 410. Further,that there is a preference by the health system's management for therelationship between throughput and schedule risk. The sequences oftasks in a care plan may be configured to be conditional 420 so that theorder or sequence may be switched, as a function of all the other systemconstraints, at one or more points in time. In an illustrative example,a given conditional branch 420 may have three sequences of tasks thatcould result or be tested: path A 422 which executes tasks 428, 430,432; path B 424 which executes tasks 446, 448; and path C 426 whichexecutes tasks 450, 452 and 454. Paths A and B are feasible pathswhere-in the assignment of resources 406 enables patient care. Thesepaths are calculated as feasible a priori to the time they are actuallyexecuted. Path C is a path that, for example, a user provides specificdirection for. An example of a cause for such an over-ride of thecontrol system's assignments is an emergency medical condition that adoctor or staff in a centralized control center orders. The disclosedsystem enables an efficient recovery from such an intervention via theautomatic re-optimization of resource assignments 406 and paths 420.

Returning to the example, the goals of the control system are to lowerthe average of, or reduce the measure of a metric such as the length ofstay of patients over an interval of time or the wait time of patientsfor scheduled services over a time interval. These goals arerealistically bound by constraints such as the number of resources madeavailable for the execution of the tasks and multiple criteria for theperformance of the healthcare delivery operations. The system and methoddisclosed herein takes as an input from the health system, what theresources are 406, their availability through time and the preferencesof management with respect to the ratio of throughput and schedule riskcriteria.

The time to complete a care plan, for example, between ending point 410and starting point 407 is desired to be minimized so that the length ofstay is reduced and average wait time for procedures is also reduced.These outcomes are achieved with efficient resource assignments and tasksequencing of the activities related to a plurality of patients 402, 404that share the resources 406 through time.

Referring now to FIG. 5, the care tasks, such as an exemplary one at 502have variation in their duration. This variation results from the skillof the care provider, the attributes of the patient, availability orperformance of the resources needed for the protocol step, for example.Traditionally, these factors which cause duration estimation varianceare accounted for by adding slack time into the schedule such that anaverage time duration is used in the scheduling of workflow or thatthere is an added increment of time in or between procedures to accountfor schedule risk. It is the summation of these slack times thataccumulate and constrain highly interdependent schedules that have ahigh coupling to shared resources and whose activities have variation.This constraint and delay aspect of the physical system occurs becauseof the many critical paths that bind the release of shared resources.For example, an operating room can be used by only one patient and a setof surgeons, nurses and apparatus at a time. A second patient'soperation cannot start until some or all the resources are madeavailable. If a first operation runs late, a second operation cannotoccur. If a first operation has variation, the second will likely notfinish on time either. A third or fourth or more operations that rely onshared resources will also likely be delayed. These delays are targetedfor reduction by managing the tasks and resources concurrently. Theaverage duration for a type of task is desired to be reduced. Thevariation of time and thus schedule risk, σ, which is the deviation froma scheduled start or finish time, is desired to be reduced. Wait timesare a byproduct of unscheduled variation. Throughput of patients isdesired to increase. However, where the institution is comfortablebalancing the relationship of throughput and schedule risk 504 as apreference, criteria or goal 506, the present control system seeks toachieve that ratio 504 through the automatic assignment of assets ortargeted user intervention via a shared decision making capability, suchas the Command Center. The shape of the relationship 504 may take manyforms. Ideally the system control can achieve maximum throughput withminimal risk. If this attribute is possible with the health deliverysystem, then it would be highly desirable to control the system to thisideal state. Due to the attributes of the tasks, resources andvariations, the relationship of throughput and variation may be suchthat there may not be an operating point where schedule risk andthroughput are jointly optimal. For example there may be a zone ofvariation where throughput is highest, yet the highest throughput onlyoccurs in a narrow window of risk. The health system management wouldset its preference for risk or throughput. The disclosed control systemwill cause the resource assignments to approach the selected preferencesof the throughput mean, μ, schedule risk, σ, throughput, andthroughput/risk trade-off value 506.

The disclosed system beneficially updates the variation in task time 502as the actual world unfolds from time before activities occur 407,through time at t₁ 408, t₂ 409 and t₃ 411. At t₁, the average durationforecast μ₁ 508 and probability 510 is known with an estimate that mayhave significant variation, where-as after tasks 412 and 418 completeand their actual duration as oppose to their forecasted duration isrealized at t₂ 409, the time forecast for the future task becomescomparatively more certain 514 with a revised average duration μ₂ 512.The reason why this revised forecast comparatively improves, relates tothe allocation of interdependent resources 406 in tasks prior to, beingmore fully realized. At t₃, after tasks 414, 416 even more tasks haverealized and thus the variation of interdependencies which could delayactivities at 502 are being reduced. The ending time t_(f) 410 is thusdependent on paths 422, 424 and 426. The user intervention into theoperations 426, is an exogenous factor that the disclosed system enablesand to recover from, but does not explicitly forecast other than throughthe historical variation observations 450 used to establish the currenttask variation forecasts. Thus the forecast for completion at step 456is a function of the duration estimations for path A 422 and B 424.

Referring to FIG. 6, in an idealized delivery system with no variation,the system scheduling and actual realization occurs exactly the same602. In the real world, tasks have variation and thus the finish 456 hasa variation in finish time 410 as depicted in the histogram 604 offrequency and time. This histogram is converted into a probabilitydensity function and then integrated to achieve a cumulative probabilityfunction 606 which characterizes the relationship between completionprobability and time. Time may be an absolute duration or the gapbetween a schedule and actual realized time or simulated time.

The task duration estimation is probabilistic 502 and is forecasted byone or any combination of four methods: actual sensed progression 724,historical duration 730 for the task that is repetitive, inference fromlike past observed durations 736 at a given institution or optionallyfrom other relevant enterprises as directed by the system ifinsufficient native historical observations are lacking, user expertise744 and simulation 740 such as a discrete event and agent-based methodsthat are virtually orchestrating some or all of the protocols andactivities.

In the protocol is a conditional step 420 where in it is possible todirect the flow of tasks in a different order or call different tasksall together. The duration of those sequences is determined with theforecasts for tasks organized in a critical path. At the conditionalstep 420, a path is chosen 422, 424 as a function of the objectives ofthe health system management with respect to throughput and schedulerisk, σ, as examples of objectives. t_(f) 410 is thus characterized foreach path 422, 424 and a most optimal path is implemented by the controlsystem at 420 and therefore a comparatively more accurate forecast mean512 and variation 514. With that added precision, all other managedtasks and resources for other patients are updated and their forecasterrors are also lowered.

There can be any number of conditional routing points 420 and tasks. Theresulting end point 456 completion with respect to time 410 will beforecasted through a full enumeration of the available paths and thepath tasks. The probability 606 of time is calculated by forecastingeach task duration using one or any combination of history, inference,process simulation and user judgment and then full enumeration throughthe conditional branches 420 and the constraints-based structure toderive the critical path duration. Replications are made of all tasksand forecasts with a Monte Carlo wrapper. The duration estimation 610for all probabilities 606 at time t₁ 610, at time t₂ 612 and time t₃614. The span of duration for each of the three curves is duration andschedule risk. The point at which the throughput and schedule risk isacceptable 608 is selected so that the main outcomes desired, such as,for example average length of stay and wait time is achieved.

IV. Example Method

The above discloses the way to aggregate cross functional task,decision, and state information for the purposes of improving hospitaloperations management by better understanding which patient will consumewhat resources in specific tasks and by enhancing the accuracy ofassumptions of the constraints-based scheduler/dynamic scheduler withsimulation that is informed by cross departmental status information.Decision makers from one or more functional departments can thus benefitfrom a trusted, comprehensive set of facts that produce betterassumptions for forecasting and optimizing control of activities intheir departments. The decision makers can over-ride the automaticcapacity and dynamic workflow control, for example, when non-computableinformation or judgment is provided to the one or more decision makerswho thus have superior context from which to then make informed localand global operations decisions and also have input into clinicaldecision makers who, synthesizing this information may change clinicalcare paths to incorporate operations opportunity or constraints.Examples of having information that would provide a superior operationsdecision from a user judgment intervention would be event data such as alarge accident or a dignitary visit or a localized breakdown in capacitythat must be given time to recover operations.

While having global, local, and probabilistic information for past,current and forecasted future improves the decision making acrossfunctional bounds of a hospital's operations, it is can also beappreciated how optimally selected pathways of alternative sets ofpathways would expedite the hospital operations decisions. In otherwords, by knowing the probabilistic outcome of making a decision BEFOREa decision is made, i.e., how it will effect both other patients, thecurrent patient's outcome, and hospital operations and resources has aprofound effect on the decision being made. This type of decision cannotbe made by humans alone, but only with the tools presented in thecurrent disclosure which are able to simulate thousands of dependenciesover multiple time ranges, across the functional departments of ahospital, automatically search the candidate space and optimally findthe best (lowest cost, lowest patient wait state time, highest hospitalcapacity and any number of one or multiple criteria, for example) andthen automatically control the allocation of resources to achieve theoptimization objective(s).

FIG. 7 illustrates a flow diagram of probabilistic cross-systemforecasting, alerting, control optimization and activation system 300according to one aspect of the present disclosure. In healthcareoperations, activities such as skilled care delivery, medical tests,medications delivery, and therapy, as well as other additional clinicalprocedures are orchestrated so as to heal the patient. Hospitaloperations seek to efficiently deliver this care at high standards, withpathways and protocols delivered in a timely manner. In certain aspects,there may be a generic structure to these clinical tasks and decisionpoints as well as nested and conditional branches to handle unforeseenvariations each circumstance presents.

The method 700 of creating predictive forecast assumptions used in thedirection of people, assets and consumables by the disclosed hospitaloperations control systems and then monitoring the resultant specificactions those resources take through time with respect to their specificlocations and duration direction in response to the initial controldirection, and then dynamically corrected if the actual hospitaloperation deviates from the ‘control signal’ is discussed. In certainaspects, the probabilistic cross-system forecasting, alerting,optimization and activation system 300 uses two operational control andoptimization loops to manage hospital activity—an inner loop allocatesthe resources of care such that the reference states and the statecondition limits of those resources is met and not exceeded while anouter loop assures that the hospital operations are progressing throughtime as required to achieve delivery of the specified care in theallotted time duration within the variable cost constraints of theenterprise. System 700 provides predictions of the care delivery processand resource state information, automated control of resources andassets in the care delivery process as well as receiving the user in theloop inputs and manages health system assignment outputs in response tothe automatic operations control or “what-if” scenario based decisionsupport.

Cross department system data gathering apparatus 702 providessystem-wide data which is collected and collated from the enterprise'sspecific departmental clinical applications, machines, and dedicatedcontrol sensors deemed to be relevant to hospital throughput and safetycontrol. These data are made accessible to the staging and productionenvironments. From these environments, derived entities 704 for use incomputation of task, resource and location attributes of the careworkflow are calculated. One consumer of this operational feedback datais prediction system 706 and another is closed loop controller 718 toassure the specified workflows, tasks, and actions are in fact,occurring in the temporal and spatial domains as anticipated byprediction system 706.

In certain aspects, prediction system 706 is comprised of fouranalytical sub-processes and is provided a patient care plan demand 708to which the hospital operations controller will optimally andprobabilistically solve for on a continuous basis in response to anever-changing presenting number of scheduled patients, their requiredcare and the changing states of care providers as assets. A preferencestate 710 of the hospital resources with respect to patient caredelivery and safety are provided for by an inner loop controller 714which in certain aspects, utilizes optimizer 720 to resolve a computedoperational plan 712 against the desired preference state 710 of theresources used in care delivery such that locations, duty cycles andsequences of the enterprise's care delivery resources are met in aduration of time being controlled for, which may span a period of timefrom the instant to the next shift, to days ahead. The beneficial resultis that patient throughput is optimized while resources such as persons,equipment, consumables and physical space do not exceed their safeoperating points so that care delivery defects are avoided. The presentcontrol system can automatically lower the care demand of executingtasks by targeted healthcare providers or receive an over-ride bydecision makers for the purposes of allowing care providers more time toexecute the requisite clinical protocols in a manner that morecompletely enables the full protocol to be safety delivered. The caredefect feedback loop is derived from sensing systems, such as opticalbased. When the defect rate is reduced, the workflow may then becontrolled to a higher throughput or intensity and/or at fewer waitstates.

Tasks, locations, and resources are controlled by the disclosed systemin an automated manner yet the direct user decision is alsoimplementable within the dynamic control in such a way as to enable thechange of direction that either agrees with the automated control systemor over-rides them and still protects the operational system outcomes.Automated and user decisions 716 with respect to tasks, locations, andtime are made and automatically disseminated to the resources of care,such as via a computer they are interacting with or an on-body devicesuch as a smart phone or other input/output device. While it is desiredthat the resources of care respond to their control directives, sincehospital operations are a dynamic system with ever-changing states ofhealth, resource availability and human capacity—the disclosed systembeneficially anticipates that the actual processes as compared to therequested actions will vary. This feedback is reconciled in closed loopcontroller 718 with a comparison of desired location and task state withactual locations and state from the enterprises' cross department systemdata gathering apparatus 702 through derived entities 704. An alert 722is provided when the current derived entities 704 and predicted computedoperational plan 712 diverge in terms of actual differences orforecasted operational risk or when infeasible operations are detectedin the instant and through the forecast interval.

In certain aspects, prediction system 706 integrates four predictiontypes. A first approach is direct sensing of controlled activities,resources, and assets via sensed state task state controller 724. Sensedstate task controller 724 uses estimated sensed duration information 726from RFID, optical based reasoning, Doppler radar based and othermodalities such as pressure, temperature, embedded equipment sensing,mobile input from consumer electronic devices such as smart phones andcomputing devices, for example. The input data are provided to crossdepartment system data gathering apparatus 702 for use by predictionsystem 706. Sensors such as optical and Doppler as well as edgecomputing devices such as smart phones have computing capabilities thatare now and can be programmed to include a prediction of state over aforecast interval and provide that prediction to compute an operationalplan 712 via prediction system 406.

A second approach is precedence and antecedent based task and resourceprobabilistic constraints-based method whose tasks are temporallyordered and whose fulfillment is conditioned upon resource availability.A simple example of such method is a Gantt analysis, however thedeterministic limitations of that method are overcome with the presentsystem which uses probabilistic durations for tasks and thus solves forprobability of completions and schedule risk. For each path sequence andstep 728, historical data 730, for example, task duration, ischaracterized and fit into a probability distribution for likelyduration of that task. A correction is made to narrow the durationestimation variance based upon the elimination of sample points relatedto task patterns such as workflows, whose paths are unlike the currentplan of task sequencing, time, resources, and entities being processedin probabilistic state estimator 732. Inferred duration estimator 734then estimates the duration for a sequence of tasks which is thenprovided as a prediction to the computed operational plan 712. Aninference may be made when there are not historical exact conditions tothe current. The root data for such inferences may be recalled from thepresent health system or another like system as selected by the system'slogic or an analyst.

A third approach is a simulation method 738 whose assumptions and stateinformation 736 is updated from the enterprise's cross department systemdata gathering apparatus 702 and derived entities 704. Simulation method738 is discrete event or agent based or a hybrid combination of agentand continuous simulation methods. Updated derived entities 704 areprovided to prediction system 706 which, in turn, provides an update ofthe current state of the hospital resources being controlled andreceives a patient care plan demand forecast 708 to which the balance ofthe disclosed health discovery system control automatically satisfies,given the state constraints and objectives of preference state 710.Simulation method 738 predicts the resource location and task durationperformance over the forecast interval and provides the simulatedduration estimation 740 to computed operational plan 712.

A forth approach is the expert opinion of one or more persons connectedto and communicating with command center console 742 whose configurableinterfaces enable the output of the disclosed decision support, theinput from the expert(s) individually or reconciled together amongststakeholders, for duration estimation 744 and/or confirmation of theautomated process control commands which are then promulgated acrossdepartments.

Each prediction of resource, patient, and physical space's use or taskduration through the specified forecast interval, may be takenindividually or fused with statistically averaged or majority win orweighted composite or probabilistically aggregated through a closed formor Monte-Carlo means in order to derive a duration forecast value rangeestimation as an input into computed operational plan 712. Comparison ofthe demand from a patient care plan 708 to the computed operational plan712 is conducted by prediction system 706 which resolves the forecastfrom which the resources are controlled in inner loop controller 714,and subject to preference state 710 and decision 716 which can either bean automated system decision or based on user intervention from thecommand center console 742, for example.

The disclosed cross departmental system provides situational awarenessof the enterprise activity status, for example, the tasks and locationsof care delivery and resource state information such as availability,location, cumulative wear or damage with respect to its maintenanceplan; physical space via physical command center console 742 whichcontain reconciled and integrated displays. The data and analyticalsequences in the disclosed control system may be configured as a seriesof web services to make the Command center console 742 accessible oncomputer networks either wired or wireless, using a computer or mobiledevices, to view and/or provide input to drive the direction of people,assets and consumables in hospital operations.

For example, a plurality of patients 804 present at a care deliveryinstitution, some of whom are scheduled while others arrive due tounplanned medical circumstances. A number of patients out of thepopulation of presenting patients 804 may have a similar care pathwayspecified upon their initial evaluation result. There may be any numberof patients 804 who could present with a known diagnosis but whoseadmittance is “gated” so as not to overwhelm the hospital's capacity.Having a similar diagnosis a sequence of patents 804 engage the careprocess beginning with a first task 806, transferring to a second task808, next proceeding to a third task 810 before a clinical decision 814is made.

A decision may result in one or more resultant next sequence of tasks814. In certain aspects, the decision D2 (812) of sequence of tasks 814involves task T6 (816) whose successor decision D4 (818) resulted in areferral R1 (820) to another clinician for decision D1 (822) with alikelihood to progress to either task T2 (808) or T3 (810) at a prioriprobabilities P1 (824) and P2 (826) respectively. For the locations,staff, and specialized assets used in T2 (808) and T3 (810) it isadvantageous to know when, for example, patent 801 would conditionallyuse those clinical services given that other patients such as patient802, are also processing through care delivery process 800.

The probability of patient 801 proceeding on path 820 towards D1 (822)is estimable at the conclusion of task T3 (810) before D2 at one leveland significantly improves after D4 (818) and further improves to nearcertainty after D1 (822). System 800 tracks the change in pathprobabilities such as P1 (824) and P2 (826) through time. Forecastedprobabilities are derived from historical patterns and inferred withstatistical confidence intervals or made explicit should a clinicaldecision/assessment be made. The specific and aggregate clinicalpathways are thus dynamically assessed and tracked in order to improvethe demand estimation in terms of quantity and time for the variousoperations in the hospital.

In certain aspects, there are a multitude of operations, such astransports, tests, medications, therapies Tn (828) that are tracked,executed, and computed over time, place, asset, people, as “tasks”. Atask is an activity that consumes time, location, people, consumables,and/or assets for a duration. The duration has a desired start andforecasted end time.

Referring to FIG. 9, the path enumeration and probability estimationmethod 900 is disclosed. The clinical pathway that patients realize maybe many. Each path has a different consumption of resources and is afunction of its sequence. The sequence results from a plan and changesto a new plan as a result of a change in a patient's health state or theeffects of a factor on the availability of resources to achieve a pathor the conditional use of a path by other patients as necessitated bytheir comparative health state. Updating where a patient is on aclinical path enables more precise allocation of resources.

Patients 804 present to the health system care providers and a resultingset of diagnosis results, from which a care plan for each patient'scondition is established. The plan may evolve at any time. Theprobability that the exact pathway trough the hospital for the durationof the treatment will be precisely known at admission is low and thusreconciling future conflicts for the use of a certain asset or resourcehas a high variability of certainty at an initial time. This imprecisionprevents a firm resource allocation and in the prior art is compensatedfor by building in the wait times and buffers in schedules. Typicallythese buffers are scheduled as slack time. It is the accumulated effectsof this slack time that the present invention targets to reduce so as toincrease throughput and lower the incidence of waiting. To achieve thisaim, a schedule is made to be dynamic, conditional and probabilisticversus a schedule set at admission. Hospital activities are thusactively controlled to advance the totality of protocols in theenterprise not based upon set schedules but to progress as the resourcesand patient states are able to. The probabilities for durations andresponses are required to reduce errant assignments and thus the mostaccurate and current probabilities are derived on a continuous basis soas to reduce control error.

In FIG. 9 an example of a single patient presenting with a diagnosis isused to illustrate a number of sequences of a pathway, each with aprobability and whose sum is 1. There is a determination that forsimilarly attributed patients and similarly available resources thepatient 801 will traverse a first pathway 902 at probability 0.03; asecond pathway 904 at p=0.05; a third pathway 906 at p=0.02, and a forthpathway 908 at p=0.9. The forth pathway 908 is known with highconfidence through the first three tasks T1, T2, and T3, up to decisionpoint D2, then depending upon the clinical or operational decision at D2(814), the patient 801 may then proceed to thousands of other paths at0.9 probability. The remaining 0.1 probability results from the sum ofthe three fully enumerated paths 902, 904, & 906 whose probabilities ofrealization sum to 0.1

Typically patients will traverse the institution to receive care 806 andthen discharge 830—thus the probability of proceeding through allfeasible care paths is 1. The network diagram 1002 for feasible paths isextensive. As an example, a single decision point 1004 to disclose themechanism of dynamically updating the probability of a patient being ona given path is shown.

Paths change by design in response to the patient's clinical response tocare and because of the resource availability used to deliver tasks anddecisions in care plans. For example, a factor E1 1006, whose patternover time is characterized by 1008, 1010, and 1012. The factor may beany one of hundreds, but for illustrative purposes, it may represent theavailability of a particular resource that would enable tasks at 1008and 1012. If a controlled resource is not available, those associatedtasks cannot be performed. A different clinical or operational decisionwould thus likely be made and the patient would experience a differentclinical pathway.

In certain aspects, there are two states, S1 and S2 for exogenous factorE1 1006: available (S1) or not available (S2). When a histogram isconstructed of that state space 1014, there is a significantly greaterportion of time in S2 than S1. When integrated to derive probability,the probability of being in S1 versus S2 is low. On average, E1 isavailable 1016.

Aspects disclosed and described herein provide the ability to attaincross departmental information to directly eliminate the forecast errordifference between E1 being available on average 1016 versus beingactually available at 1008 and 1012 which are explicit states and timesso that a future path is determinable, actionable, and schedulable as afunction and specificity commensurate with the confidence interval,which at all times is higher than the present art which usesdeterministic durations and not continuously updated as the actual timeunfolds nor through discrete or agent based simulation—whose assumptionsand states are updated dynamically so that simulation based forecastsare also more accurate.

In another example, a different factor E2 1018 may be a patient's healthstate with respect to one or more clinical metrics or measures. As isthe case with E1 1006, as time proceeds, so does the value of E2. Thestate S3 1020 of E2 when converted into a histogram then integrates intoa probability density function 1022 and has a statistical mean value1024. There is a forecast error between this average state variable S31024 and the probability that S3 may be anywhere in the state spacedefined by its probability density function 1022.

For a patient 801 at time zero, looking forward into a network of likelypaths the probability of being realized through a specific path ischaracterized by 902, 904, 906, and 908. As an example, as clinicalactivity progresses, finishing task 3 at time t_(n−2) 1026 with E1 1006not available and E2 at an actual clinical metric level indication, theprobability that any of the available clinical path enumerations is moredeterminable than at the start of T1 806. At a next time intervalt_(n−1) 1028, which for illustrative purposes is driving decision pointD2 812, the probability of knowing which next step T4, T5, T6 improvesin specificity.

At a time, for example “now” 1030, where decision point D2 is now known1032 and the probabilities for which future path will realize changeagain. At t_(n) 1030, it is 100% certain that a specific care path 1032has been reached and an explicit path to the current location and carepath is known. From past like-attribute patients it is 50% likely a carepath through T4 and its successors 1034; 35% likely through T5 and itssuccessors 1036; and 15% likely through T6 and its successors 1038. Atthe next time increment t_(n+1) 1040, decision D2 and the next task areknown, therefore the probabilities change again for the sequences offuture tasks. This dynamic and ongoing aggregation of updates fromacross departments coupled with parsed down conditional or potentialpathways as time is realized benefits the forecast accuracy with respectto hospital operations activity, staffing, and usage forecasting.

With the state of care pathways made explicit and the likelihood offuture paths better appreciated across the various functions in thehospital, the demand estimates on future tasks, providers, assets isglobally understandable and actionable by either the control system'sautomatic assignments or by user direction via the command centerinterface. Correct scheduling changes, putting the current patient onthe optimal care path can thus be made with minimal or no disruption ofthe rest of the enterprise-wide system.

Thus, the disclosed cross departmental system provides an aggregatecross-functional task, decision, and state information for the purposesof improving hospital operations management by better understandingwhich patients will consume specific resources during each specifictask. Human decision makers from one or several functional departmentscan thus use a trusted set of facts and better understand demandforecasts for the activities throughout their departments in theinstances that the system's automated assignment control is intervenedon. These decision makers can then make informed local and globaloperations decisions and also have input into clinical decision makerswho, synthesizing the information may change clinical care plans toincorporate operations opportunities or constraints.

While having global, local, and probabilistic information improves theautomated control of resources or the act of user decision making acrossfunctional bounds of a hospital's operations, it also demonstrates howoptimally selected pathways or alternative sets of pathways would impactand expedite the hospital operations decisions.

Referring now to FIG. 11, a set of decisions and tasks is arranged insequence and time with antecedents and predicate relationships. The‘critical path’ is desired to be known so that for operations purposes,workflows and schedules can be directed to achieve a task of decisionpoint to occur at a beneficial point in time, thus managing theoperational activities so as to achieve hospital throughput, minimizedelays, improve resource utilization, lower costs and improve thepatient's experience by proper expectation setting and communication ofstatus.

Temporal and logical relationships are drawn 1100 incorporating themethods described above across departments and time, therebyillustrating how the benefits of manual and automatic resource levelingand dynamic workflow and scheduling is benefited.

Before decision point D1 1102, there are probabilities of three paths oftasks occurring before a second decision point is reached. Probabilityof a first path P1 1104, a second path P2 1106 and a third path P3 1108may be known and an expected value for a cross function of thoseprobabilities and the probabilistic sums of the tasks T_(n) 1110 in eachpath to derive an expected duration.

Certain aspects of the present disclosure enable improving hospitaloperations by calculating the path dependent duration probability acrossdepartments. For example, a path P1 1104 is deemed to be viable anddesirable for a given patient's care plan next steps. On path P1 is taskT_(n) 1110 whose parameters in terms of resource consumption andprobabilistic duration are known. The use of task T_(n) 1110 is checkedfor more optimal placement or resource scheduling by invoking anoperations optimization engine 720. Task T_(n) 1110 parameters are sentfrom the current hospital operations system to the optimization engine720.

The current hospital operations system is comprised of crossdepartmental data 702 fed via messaging system such as one capable ofinterpreting HL7 messages for example, into a computer or server orcloud-based computing system and interfaced with one or more userinterfaces 742.

Resource constraints, re-optimized workflow scenarios and schedulealternatives generated with the operations optimization engine 720 aremade available via Command Center 742 or via computers and/or mobiledevices for consideration by automated assignments or one or moredecision makers or a hybrid of automation and user interactions—eachmethod impacting one or more departments and the institution's capacityand/or safety at large. Decision D1 1102 and D2 1112 benefit fromunderstanding how a decision made ahead of activity impacts the broadercontext and may choose one path over the other in response to stabilizeoperations or improve a patient's experience.

In reality, hospital operations decision makers are overwhelmed with thepossible combinations of hundreds of patients and potential pathways,each with conditions and interdependencies. For example, in a hospitalenvironment it can be conservatively estimated that it would take personyears to compute and rank the possible combinations to choose thecorrect combination before the process of reconciling other person'sopinions would begin. The present system calculates the comparativeimpact of one or more departmental delays or early finishes across thehospital's departments in approximately 22 seconds, solving with theobjectives and constraints from more than one department or decisionmaker.

Referring to FIG. 12, a course ranking method 1200 is presented for adynamic environment spanning a plurality of time periods using anexample impact metric related to time duration for services deliverywhich is typically a key hospital operations process indicator.

At a first time, t, pathways 1202, 1210, of sequences of tasks anddecision which can be taken for one or more patients are possible. Afirst pathway 1204 of tasks and decisions has a duration 1206 thatconsumes time and resources which impacts 1208 hospital operations. Analternate path 1210 results in achieving a clinical goal and consumesless time 1212 with equal clinical impact. The second path 1210 is sentto the operations optimization engine 720 for feasibility. In anotherexample, a first 1202 and second 1210 path appear to be roughlyequivalent clinically and it is desired to know which path isoperationally superior. Both scenarios are passed to the operationsoptimization engine 720 for an assessment of schedule risk, cost orother operational criteria.

In the next instant of time 1214 the state of the hospital's caredelivery flow changes and the evaluation process begins again; firstwith a rough estimate and then a precise evaluation of a subset ofscenarios to calculate schedules, resource loading, and throughput, forexample.

Departmental management typically ensures that service levels are metfor clinical tasks conducted in those functional areas. The actions ofone or more departments have an effect on other departments and therebythe overall hospital operations performance may degrade because theinteraction effects were not used as a criteria in a department. Certainaspects of the present disclosure overcome these instances where‘locally’ optimal or ‘local’ delays impact the broader institution sincemore informed clinical pathways, workflows, and schedules are enabledthat more precisely place patients, providers, and assets for morecoordinated operations.

Typical operations management systems monitor ‘what is’ such as bedoccupancy and in some instances, infer the future state by using averagedurations for activities. Missing are considerations that multiple pathsfor clinical activity are included in those averages as well as taskdurations taken as average. The present system improves operations byconsidering paths, resource constraints and updates the forecast basedupon cross-departmental status updates.

Flowcharts representative of example machine readable instructions forimplementing the example system 300 of FIG. 3 are shown in FIG. 7. Inthese examples, the machine readable instructions comprise a program forexecution by a processor such as processor 1312 shown in the exampleprocessor platform 1300 discussed below in connection with FIG. 13. Theprogram can be embodied in software stored on a tangible computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, adigital versatile disk (DVD), a BLU-RAY™ disk, or a memory associatedwith processor 1312, but the entire program and/or parts thereof couldalternatively be executed by a device other than processor 1312 and/orembodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 7, many other methods of implementing the example system-wideprobabilistic alerting and activation can alternatively be used. Forexample, the order of execution of the blocks can be changed, and/orsome of the blocks described can be changed, eliminated, or combined.

As mentioned above, the example processes of FIG. 8 can be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIG. 7 can be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

V. COMPUTING DEVICE

The subject matter of this description may be implemented as stand-alonesystem or for execution as an application capable of execution by one ormore computing devices. The application (e.g., webpage, downloadableapplet or other mobile executable) can generate the various displays orgraphic/visual representations described herein as graphic userinterfaces (GUIs) or other visual illustrations, which may be generatedas webpages or the like, in a manner to facilitate interfacing(receiving input/instructions, generating graphic illustrations) withusers via the computing device(s).

Memory and processor as referred to herein can be stand-alone orintegrally constructed as part of various programmable devices,including for example a desktop computer or laptop computer hard-drive,field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), application-specific standard products (ASSPs),system-on-a-chip systems (SOCs), programmable logic devices (PLDs), etc.or the like or as part of a Computing Device, and any combinationthereof operable to execute the instructions associated withimplementing the method of the subject matter described herein.

Computing device as referenced herein may include: a mobile telephone; acomputer such as a desktop or laptop type; a Personal Digital Assistant(PDA) or mobile phone; a notebook, tablet or other mobile computingdevice; or the like and any combination thereof.

Computer readable storage medium or computer program product asreferenced herein is tangible (and alternatively as non-transitory,defined above) and may include volatile and non-volatile, removable andnon-removable media for storage of electronic-formatted information suchas computer readable program instructions or modules of instructions,data, etc. that may be stand-alone or as part of a computing device.Examples of computer readable storage medium or computer programproducts may include, but are not limited to, RAM, ROM, EEPROM, Flashmemory, CD-ROM, DVD-ROM or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired electronicformat of information and which can be accessed by the processor or atleast a portion of the computing device.

The terms module and component as referenced herein generally representprogram code or instructions that causes specified tasks when executedon a processor. The program code can be stored in one or more computerreadable mediums.

Network as referenced herein may include, but is not limited to, a widearea network (WAN); a local area network (LAN); the Internet; wired orwireless (e.g., optical, Bluetooth, radio frequency (RF)) network; acloud-based computing infrastructure of computers, routers, servers,gateways, etc.; or any combination thereof associated therewith thatallows the system or portion thereof to communicate with one or morecomputing devices.

The term user and/or the plural form of this term is used to generallyrefer to those persons capable of accessing, using, or benefiting fromthe present disclosure.

FIG. 13 is a block diagram of an example processor platform 1300 capableof executing the instructions of FIG. 7 to implement the example of FIG.3. The processor platform 1300 can be, for example, a server, a personalcomputer, a mobile device (e.g., a cell phone, a smart phone, a tabletsuch as an IPAD™), a personal digital assistant (PDA), an Internetappliance, or any other type of computing device.

The processor platform 1300 of the illustrated example includes aprocessor 1312. Processor 1312 of the illustrated example is hardware.For example, processor 1312 can be implemented by one or more integratedcircuits, logic circuits, microprocessors or controllers from anydesired family or manufacturer.

Processor 1312 of the illustrated example includes a local memory 1313(e.g., a cache). Processor 1312 of the illustrated example is incommunication with a main memory including a volatile memory 1314 and anon-volatile memory 1316 via a bus 1318. Volatile memory 1314 can beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 1316 can be implemented by flash memory and/or any other desiredtype of memory device. Access to main memory 1314, 1316 is controlled bya memory controller.

Processor platform 1300 of the illustrated example also includes aninterface circuit 1320. Interface circuit 1320 can be implemented by anytype of interface standard, such as an Ethernet interface, a universalserial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1322 are connectedto the interface circuit 1320. Input device(s) 1322 permit(s) a user toenter data and commands into processor 1312. The input device(s) can beimplemented by, for example, an audio sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1324 are also connected to interface circuit1320 of the illustrated example. Output devices 1324 can be implemented,for example, by display devices (e.g., a light emitting diode (LED), anorganic light emitting diode (OLED), a liquid crystal display, a cathoderay tube display (CRT), a touchscreen, a tactile output device, a lightemitting diode (LED), a printer and/or speakers). Interface circuit 1320of the illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

Interface circuit 1320 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1326 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

Processor platform 1300 of the illustrated example also includes one ormore mass storage devices 1328 for storing software and/or data.Examples of such mass storage devices 1328 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 1332 of FIG. 5 can be stored in mass storage device1328, in volatile memory 1314, in the non-volatile memory 1316, and/oron a removable tangible computer readable storage medium such as a CD orDVD.

VI. Conclusion

Thus, certain examples provide a clinical knowledge and control systemthat enables healthcare institutions to improve performance, reducecost, touch more people, and deliver a higher quality of care becausemore aspects of the protocols are executed, across the departments ofthe enterprise. In certain examples, the clinical knowledge and controlsystem enables healthcare delivery organizations to improve performanceagainst their quality targets, resulting in better patient care at alow, appropriate cost.

Certain examples facilitate improved control over process. For example,a healthcare operations control system including a prediction engineintegrating data from one or more departments and healthcare systems ina healthcare environment is enabled for the control of operationalthroughput, wait-times, and schedule risk. The example control systemalso includes identifying controllable and non-controllable resourcesand a plurality of conditional and non-conditional clinical protocoltasks which are subsequently directed. The example control system alsoincludes receiving one or more operating objectives related to one ormore of operational throughput, wait-times, and schedule risk andmonitoring the execution, with respect to the objectives, of theplurality of clinical protocol tasks in a clinical environment by thecare delivery resources including people and physical assets. Theexample control system further includes predicting a) activity of theone or more care delivery resources and patients in the plurality ofclinical protocol tasks for one or more time periods and b) a change ofactivity of patients and care delivery resources with respect to theclinical protocol tasks over a selectable interval of time. The examplecontrol system also includes automatically optimizing present and futureactions of the controllable care delivery resources to mitigate thepredicted non-value-added idle time, throughput constraint and workloadof care providers to control a flow of patients through clinicalprotocols and a risk of achieving specified resource efficiency acrossthe plurality of clinical protocols. The example control systemadditionally includes triggering the one or more mitigating actions withthe respect to the plurality of clinical protocols; and monitoringeffects of the mitigating actions and adjust resource assignments basedon the effects of the mitigating actions.

Certain examples leverage information technology infrastructure tostandardize and centralize data across an organization. In certainexamples, this allows for cross-system probabilistic alerting andactivation that automatically controls the resources and conditionallyallocates capacity across departments to achieve a minimal wait statethrough an interval of time.

Technical effects of the subject matter described above may include, butis not limited to, providing system 300 and method 700 which allow forcross-system probabilistic alerting and activation that automaticallycontrols the resources and conditionally allocates capacity acrossdepartments to achieve a minimal wait state through an interval of time.

Moreover, the system and method of this subject matter described hereincan be configured to provide an ability to better understand largevolumes of data generated by devices across diverse locations, in amanner that allows such data to be more easily exchanged, sorted,analyzed, acted upon, and learned from to achieve more strategicdecision-making, more value from technology spend, improved quality andcompliance in delivery of services, better customer or businessoutcomes, and optimization of operational efficiencies in productivity,maintenance and management of assets (e.g., devices and personnel)within complex workflow environments that may involve resourceconstraints across diverse locations.

This written description uses examples to disclose the subject matter,and to enable one skilled in the art to make and use the invention. Thepatentable scope of the subject matter is defined by the followingclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims if they have structural elements that do not differ from theliteral language of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

What is claimed is:
 1. A computerized healthcare operations controlsystem comprising: a prediction engine including a processor tointegrate data from a plurality of healthcare systems across departmentsin a healthcare environment to facilitate control of operationalthroughput, wait-times, and schedule risk in the healthcare environment,the processor configured to at least: identify care delivery resourcesand distinguish between controllable resources and non-controllableresources in the care delivery resources by parsing data received fromthe care delivery resources to define a set of controllable resourcesdifferentiated from a set of non-controllable resources, wherein the setof controllable resources is able to be directly controlled by theprediction engine and wherein the set of non-controllable resources isnot able to be directly controlled by the prediction engine; receive oneor more operating objectives related to one or more of operationalthroughput, wait-times, or schedule risk; monitor execution, withrespect to the one or more operating objectives, of a plurality of firstclinical protocol tasks in the healthcare environment by the caredelivery resources; predict a) activity of the care delivery resourcesand patients in a second plurality of clinical protocol tasks for one ormore time periods and b) a change of activity of patients and caredelivery resources with respect to the second plurality of clinicalprotocol tasks over a selectable interval of time; automatically adjustconfiguration of the set of controllable resources with respect to thesecond plurality of clinical protocol tasks to mitigate a predicteddeviation from one or more operating objectives related to one or moreof operational throughput, wait-times, or schedule risk; and monitor aneffect of the adjusted configuration of the set of controllableresources across the departments in the healthcare environment, whereinthe processor of the prediction engine includes an inner loop controllerto allocate and control, based on the predicted activity and change ofactivity, controllable resources to satisfy states associated with theclinical protocol tasks for a particular department, and an outer loopcontroller to manage care delivery constraints across the departments inthe healthcare environment by monitoring inner loop controller output intemporal and spatial domains and providing feedback to the inner loopcontroller.
 2. The system of claim 1, wherein the prediction engine isfurther configured to display the monitoring of the protocol.
 3. Thesystem of claim 1, wherein the prediction engine is further configuredto display mitigating actions.
 4. The system of claim 1, wherein theadjusted configuration is overridden and the system is to re-adjustconfiguration of the set of controllable resources.
 5. The system ofclaim 4, wherein reasons for the override are recorded and provided asfeedback.
 6. The system of claim 1, wherein the care delivery resourcescomprise at least one of location, personnel or equipment.
 7. The systemof claim 1, wherein the prediction engine uses one or more of a discreteevent simulation, an agent-based simulation, a regression, a patterninference, a precedent-based constraints program informed by historicaldurations or expert inputs.
 8. A computer-implemented method forcontrolling healthcare operations via a configured processor in aprediction engine, the method comprising: integrating data from aplurality of healthcare systems across departments in a healthcareenvironment to facilitate control of operational throughput, wait-times,and schedule risk in the healthcare environment; identifying caredelivery resources and distinguish between controllable resources andnon-controllable resources in the care delivery resources by parsingdata received from the care delivery resources to define a set ofcontrollable resources differentiated from a set of non-controllableresources, wherein the set of controllable resources is able to bedirectly controlled by the prediction engine and wherein the set ofnon-controllable resources is not able to be directly controlled by theprediction engine; receiving one or more operating objectives related toone or more of operational throughput, wait-times, or schedule risk;monitoring execution, with respect to the one or more operatingobjectives, of a plurality of first clinical protocol tasks in thehealthcare environment by the care delivery resources; predicting a)activity of the care delivery resources and patients in a secondplurality of clinical protocol tasks for one or more time periods and b)a change of activity of patients and care delivery resources withrespect to the second plurality of clinical protocol tasks over aselectable interval of time; automatically adjusting configuration ofthe set of controllable resources with respect to the second pluralityof clinical protocol tasks to mitigate a predicted deviation from one ormore operating objectives related to one or more of operationalthroughput, wait-times, or schedule risk; and monitoring an effect ofthe adjusted configuration of the set of controllable resources acrossthe departments in the healthcare environment, wherein the processor ofthe prediction engine includes an inner loop controller to allocate andcontrol, based on the predicted activity and change of activity,controllable resources to satisfy states associated with the clinicalprotocol tasks for a particular department, and an outer loop controllerto manage care delivery constraints across the departments in thehealthcare environment by monitoring inner loop controller output intemporal and spatial domains and providing feedback to the inner loopcontroller.
 9. The method of claim 8, wherein the prediction engine isfurther configured to display the monitoring of the protocol.
 10. Themethod of claim 8, wherein the prediction engine is further configuredto display mitigating actions.
 11. The method of claim 8, wherein theadjusted configuration is overridden and the configuration of the set ofof controllable resources is re-adjusted.
 12. The method of claim 11,wherein reasons for the override are recorded and provided as feedback.13. The method of claim 8, wherein the care delivery resources compriseat least one of location, personnel or equipment.
 14. The method ofclaim 8, wherein the prediction engine uses one or more of a discreteevent simulation, an agent-based simulation, a regression, a patterninference, a precedent-based constraints program informed by historicaldurations or expert inputs.
 15. A computer-readable storage devicehaving a set of instructions for execution on a computer, the set ofinstructions, when executed, causing and configuring the computer toimplement a method, the method comprising: integrating data from aplurality of healthcare systems across departments in a healthcareenvironment to facilitate control of operational throughput, wait-times,and schedule risk in the healthcare environment; identifying caredelivery resources and distinguish between controllable resources andnon-controllable resources in the care delivery resources by parsingdata received from the care delivery resources to define a set ofcontrollable resources differentiated from a set of non-controllableresources, wherein the set of controllable resources is able to bedirectly controlled by the computer and wherein the set ofnon-controllable resources is not able to be directly controlled by thecomputer; receiving one or more operating objectives related to one ormore of operational throughput, wait-times, or schedule risk; monitoringexecution, with respect to the one or more operating objectives, of aplurality of first clinical protocol tasks in the healthcare environmentby the care delivery resources; predicting a) activity of the caredelivery resources and patients in a second plurality of clinicalprotocol tasks for one or more time periods and b) a change of activityof patients and care delivery resources with respect to the secondplurality of clinical protocol tasks over a selectable interval of time;automatically adjusting configuration of the set of controllableresources with respect to the second plurality of clinical protocoltasks to mitigate a predicted deviation from one or more operatingobjectives related to one or more of operational throughput, wait-times,or schedule risk; and monitoring an effect of the adjusted configurationof the set of controllable resources monitor across the departments inthe healthcare environment, wherein the computer includes an inner loopcontroller to allocate and control, based on the predicted activity andchange of activity, controllable resources to satisfy states associatedwith the clinical protocol tasks for a particular department, and anouter loop controller to manage care delivery constraints across thedepartments in the healthcare environment by monitoring inner loopcontroller output in temporal and spatial domains and providing feedbackto the inner loop controller.
 16. The computer-readable storage deviceof claim 15, wherein the computer is further configured to display themonitoring of the protocol.
 17. The computer-readable storage device ofclaim 15, wherein the computer is further configured to displaymitigating actions.
 18. The computer-readable storage device of claim17, wherein the adjusted configuration is overridden and theconfiguration of the set of controllable resources is to be re-adjusted.19. The computer-readable storage device of claim 18, wherein reasonsfor the override are recorded and provided as feedback.
 20. Thecomputer-readable storage device of claim 15, wherein the care deliveryresources comprise at least one of location, personnel or equipment.